feat[next]: Remove dace_iterator backend and pass_manager_legacy (#1753)

The dace orchestration tests are temporarily skipped until #1742 is
merged.
The dace backend with SDFG optimization is temporarily disabled in unit
tests until #1639 is merged.
A second PR will reorganize the files in dace backend module.

src/gt4py/next/program_processors/runners/dace.py

@@ -8,45 +8,34 @@
 
 import factory
 
+import gt4py._core.definitions as core_defs
+import gt4py.next.allocators as next_allocators
 from gt4py.next import backend
+from gt4py.next.otf import workflow
 from gt4py.next.program_processors.runners.dace_fieldview import workflow as dace_fieldview_workflow
-from gt4py.next.program_processors.runners.dace_iterator import workflow as dace_iterator_workflow
 from gt4py.next.program_processors.runners.gtfn import GTFNBackendFactory
 
 
-class DaCeIteratorBackendFactory(GTFNBackendFactory):
+class DaCeFieldviewBackendFactory(GTFNBackendFactory):
+    class Meta:
+        model = backend.Backend
+
     class Params:
-        otf_workflow = factory.SubFactory(
-            dace_iterator_workflow.DaCeWorkflowFactory,
-            device_type=factory.SelfAttribute("..device_type"),
-            use_field_canonical_representation=factory.SelfAttribute(
-                "..use_field_canonical_representation"
-            ),
+        name_device = "cpu"
+        name_cached = ""
+        name_postfix = ""
+        gpu = factory.Trait(
+            allocator=next_allocators.StandardGPUFieldBufferAllocator(),
+            device_type=next_allocators.CUPY_DEVICE or core_defs.DeviceType.CUDA,
+            name_device="gpu",
         )
-        auto_optimize = factory.Trait(
-            otf_workflow__translation__auto_optimize=True, name_postfix="_opt"
+        cached = factory.Trait(
+            executor=factory.LazyAttribute(
+                lambda o: workflow.CachedStep(o.otf_workflow, hash_function=o.hash_function)
+            ),
+            name_cached="_cached",
         )
-        use_field_canonical_representation: bool = False
-
-    name = factory.LazyAttribute(
-        lambda o: f"run_dace_{o.name_device}{o.name_temps}{o.name_cached}{o.name_postfix}.itir"
-    )
-
-    transforms = backend.LEGACY_TRANSFORMS
-
-
-run_dace_cpu = DaCeIteratorBackendFactory(cached=True, auto_optimize=True)
-run_dace_cpu_noopt = DaCeIteratorBackendFactory(cached=True, auto_optimize=False)
-
-run_dace_gpu = DaCeIteratorBackendFactory(gpu=True, cached=True, auto_optimize=True)
-run_dace_gpu_noopt = DaCeIteratorBackendFactory(gpu=True, cached=True, auto_optimize=False)
-
-itir_cpu = run_dace_cpu
-itir_gpu = run_dace_gpu
-
-
-class DaCeFieldviewBackendFactory(GTFNBackendFactory):
-    class Params:
+        device_type = core_defs.DeviceType.CPU
         otf_workflow = factory.SubFactory(
             dace_fieldview_workflow.DaCeWorkflowFactory,
             device_type=factory.SelfAttribute("..device_type"),
@@ -55,11 +44,16 @@ class Params:
         auto_optimize = factory.Trait(name_postfix="_opt")
 
     name = factory.LazyAttribute(
-        lambda o: f"run_dace_{o.name_device}{o.name_temps}{o.name_cached}{o.name_postfix}.gtir"
+        lambda o: f"run_dace_{o.name_device}{o.name_cached}{o.name_postfix}"
     )
 
+    executor = factory.LazyAttribute(lambda o: o.otf_workflow)
+    allocator = next_allocators.StandardCPUFieldBufferAllocator()
     transforms = backend.DEFAULT_TRANSFORMS
 
 
-gtir_cpu = DaCeFieldviewBackendFactory(cached=True, auto_optimize=False)
-gtir_gpu = DaCeFieldviewBackendFactory(gpu=True, cached=True, auto_optimize=False)
+run_dace_cpu = DaCeFieldviewBackendFactory(cached=True, auto_optimize=True)
+run_dace_cpu_noopt = DaCeFieldviewBackendFactory(cached=True, auto_optimize=False)
+
+run_dace_gpu = DaCeFieldviewBackendFactory(gpu=True, cached=True, auto_optimize=True)
+run_dace_gpu_noopt = DaCeFieldviewBackendFactory(gpu=True, cached=True, auto_optimize=False)

src/gt4py/next/program_processors/runners/dace_common/dace_backend.py

@@ -24,7 +24,7 @@
     cp = None
 
 
-def _convert_arg(arg: Any, sdfg_param: str, use_field_canonical_representation: bool) -> Any:
+def _convert_arg(arg: Any, sdfg_param: str) -> Any:
     if not isinstance(arg, gtx_common.Field):
         return arg
     if len(arg.domain.dims) == 0:
@@ -41,26 +41,14 @@ def _convert_arg(arg: Any, sdfg_param: str, use_field_canonical_representation:
         raise RuntimeError(
             f"Field '{sdfg_param}' passed as array slice with offset {dim_range.start} on dimension {dim.value}."
         )
-    if not use_field_canonical_representation:
-        return arg.ndarray
-    # the canonical representation requires alphabetical ordering of the dimensions in field domain definition
-    sorted_dims = dace_utils.get_sorted_dims(arg.domain.dims)
-    ndim = len(sorted_dims)
-    dim_indices = [dim_index for dim_index, _ in sorted_dims]
-    if isinstance(arg.ndarray, np.ndarray):
-        return np.moveaxis(arg.ndarray, range(ndim), dim_indices)
-    else:
-        assert cp is not None and isinstance(arg.ndarray, cp.ndarray)
-        return cp.moveaxis(arg.ndarray, range(ndim), dim_indices)
-
-
-def _get_args(
-    sdfg: dace.SDFG, args: Sequence[Any], use_field_canonical_representation: bool
-) -> dict[str, Any]:
+    return arg.ndarray
+
+
+def _get_args(sdfg: dace.SDFG, args: Sequence[Any]) -> dict[str, Any]:
     sdfg_params: Sequence[str] = sdfg.arg_names
     flat_args: Iterable[Any] = gtx_utils.flatten_nested_tuple(tuple(args))
     return {
-        sdfg_param: _convert_arg(arg, sdfg_param, use_field_canonical_representation)
+        sdfg_param: _convert_arg(arg, sdfg_param)
         for sdfg_param, arg in zip(sdfg_params, flat_args, strict=True)
     }
 
@@ -154,10 +142,10 @@ def get_sdfg_conn_args(
 
 def get_sdfg_args(
     sdfg: dace.SDFG,
+    offset_provider: gtx_common.OffsetProvider,
     *args: Any,
     check_args: bool = False,
     on_gpu: bool = False,
-    use_field_canonical_representation: bool = True,
     **kwargs: Any,
 ) -> dict[str, Any]:
     """Extracts the arguments needed to call the SDFG.
@@ -166,10 +154,10 @@ def get_sdfg_args(
 
     Args:
         sdfg:               The SDFG for which we want to get the arguments.
+        offset_provider:    Offset provider.
     """
-    offset_provider = kwargs["offset_provider"]
 
-    dace_args = _get_args(sdfg, args, use_field_canonical_representation)
+    dace_args = _get_args(sdfg, args)
     dace_field_args = {n: v for n, v in dace_args.items() if not np.isscalar(v)}
     dace_conn_args = get_sdfg_conn_args(sdfg, offset_provider, on_gpu)
     dace_shapes = _get_shape_args(sdfg.arrays, dace_field_args)

src/gt4py/next/program_processors/runners/dace_common/utility.py

@@ -9,7 +9,7 @@
 from __future__ import annotations
 
 import re
-from typing import Final, Literal, Optional, Sequence
+from typing import Final, Literal, Optional
 
 import dace
 
@@ -96,10 +96,3 @@ def filter_connectivity_types(
         for offset, conn in offset_provider_type.items()
         if isinstance(conn, gtx_common.NeighborConnectivityType)
     }
-
-
-def get_sorted_dims(
-    dims: Sequence[gtx_common.Dimension],
-) -> Sequence[tuple[int, gtx_common.Dimension]]:
-    """Sort list of dimensions in alphabetical order."""
-    return sorted(enumerate(dims), key=lambda v: v[1].value)

src/gt4py/next/program_processors/runners/dace_common/workflow.py

@@ -150,9 +150,9 @@ def decorated_program(
 
         sdfg_args = dace_backend.get_sdfg_args(
             sdfg,
+            offset_provider,
             *args,
             check_args=False,
-            offset_provider=offset_provider,
             on_gpu=on_gpu,
             use_field_canonical_representation=use_field_canonical_representation,
         )